Entity (term/phrase) weighting is also a common approach to emphasize the key words in the query and improve the retrieval performance. To predict the weight of each entity, conventional methods use the corpus statistics based methods like TF-IDF, BM25, and SRNM [1]}, [2]} to compute the scores of entities. Recently, neural network based methods have been widely studied: [3]} uses a supervised query performance prediction method to learn query term weighting; [4]} utilizes a random-walk model to learn latent representations for candidates to capture the relative importance between entities;
[5]} uses pre-trained language models to learn sparse entity representations in order to better predict the importance of each entity. However, these works haven't utilized the expansion and term weighting simultaneously. DeepImapct [6]} first expands the documents using docT5query [7]}, and then uses a contrastive loss to predict the weight of each term in the query and document. However, DeepImapct focuses on generating the expansions for long documents (e.g. a paragraph), but it has not been expanded to short query scenarios.
